arules: Mining Association Rules using R
R is a free software environment for statistical computing and graphics widely used for data mining. Association rule mining (see research page on association rules) is one of the most successful data mining techniques. The R add-on package arules implements the basic infrastructure for creating and manipulating transaction databases and basic algorithms to efficiently find and analyze association rules. Several more packages provide provide additional functionality like frequent sequence mining, association rule visualization and associative classification techniques. Compared to other tools, the arules framework is fully integrated, implements the latest approaches and has the vast functionality of R for further analysis of found patterns at its disposal.
- Christian Buchta (Vienna University of Economics and Business)
- Sudheer Chelluboina (SMU)
- Bettina Grün (Johannes Kepler University Linz)
- Michael Hahsler (lead developer, SMU)
- Ian Johnson (SMU)
- Kurt Hornik (Vienna University of Economics and Business)
package for mining association rules and frequent itemsets.
- arulesViz: A
package for visualizing association rules based on package arules.
- arulesSequences: Add-on for arules to handle and mine
- arulesCBA: Add-on for arules to provide classification based on
association rules (CBA).
- arulesNBMiner: Add-on for arules to mine NB-frequent itemsets
(see paper) and NB-precise rules.
- Michael Hahsler. Grouping association rules using lift. In C. Iyigun, R. Moghaddess, and A. Oztekin, editors, 11th INFORMS Workshop on Data Mining and Decision Analytics (DM-DA 2016), November 2016. [ .pdf ]
- Michael Hahsler and Radoslaw Karpienko. Visualizing association rules in hierarchical groups. Journal of Business Economics, pages 1--19, May 2016. [ DOI | .pdf ]
- Anurag Nagar, Michael Hahsler, and Hisham Al-Mubaid. Association rule mining of gene ontology annotation terms for SGD. In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, August 2015. [ DOI | .pdf ]
- Jörg Lässig and Michael Hahsler. Cooperative data analysis in supply chains using selective information disclosure. In Brian Borchers, J. Paul Brooks, and Laura McLay, editors, Operations Research and Computing: Algorithms and Software for Analytics, 14th INFORMS Computing Society Conference (ICS2015). INFORMS, January 2015. [ at the publisher ]
- Michael Hahsler and Sudheer Chelluboina. Visualizing association rules in hierarchical groups. Unpublished. Presented at the 42nd Symposium on the Interface: Statistical, Machine Learning, and Visualization Algorithms (Interface 2011), June 2011. [ .pdf ]
- Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977--1981, 2011. [ at the publisher ]
- Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association rule generation. Computational Statistics, 23(2):303--315, April 2008. [ DOI | at the publisher | .pdf ]
- Michael Hahsler and Kurt Hornik. Building on the arules infrastructure for analyzing transaction data with R. In R. Decker and H.-J. Lenz, editors, Advances in Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pages 449--456. Springer-Verlag, 2007. [ DOI | .pdf ]
- Michael Hahsler and Kurt Hornik. New probabilistic interest measures for association rules. Intelligent Data Analysis, 11(5):437--455, 2007. [ at the publisher | .pdf ]
- Thomas Reutterer, Michael Hahsler, and Kurt Hornik. Data Mining und Marketing am Beispiel der explorativen Warenkorbanalyse. Marketing ZFP, 29(3):165--181, 2007. [ at the publisher ]
- Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery, 13(2):137--166, September 2006. [ DOI | at the publisher | .pdf ]
- Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Warenkorbanalyse mit Hilfe der Statistik-Software R. In Peter Schnedlitz, Renate Buber, Thomas Reutterer, Arnold Schuh, and Christoph Teller, editors, Innovationen in Marketing, pages 144--163. Linde-Verlag, 2006. [ at the publisher | .pdf ]
- Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Implications of probabilistic data modeling for mining association rules. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, Studies in Classification, Data Analysis, and Knowledge Organization, pages 598--605. Springer-Verlag, 2006. [ at the publisher | .pdf ]
- Michael Hahsler, Bettina Grün, and Kurt Hornik. arules -- A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15):1--25, October 2005. [ DOI | at the publisher ]
- Andreas Geyer-Schulz and Michael Hahsler. Comparing two recommender algorithms with the help of recommendations by peers. In O.R. Zaiane, J. Srivastava, M. Spiliopoulou, and B. Masand, editors, WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles 4th International Workshop, Edmonton, Canada, July 2002, Revised Papers, Lecture Notes in Computer Science LNAI 2703, pages 137--158. Springer-Verlag, 2003. (Revised version of the WEBKDD 2002 paper “Evaluation of Recommender Algorithms for an Internet Information Broker based on Simple Association Rules and on the Repeat-Buying Theory”). [ at the publisher | .pdf ]
- Andreas Geyer-Schulz and Michael Hahsler. Evaluation of recommender algorithms for an internet information broker based on simple association rules and on the repeat-buying theory. In Brij Masand, Myra Spiliopoulou, Jaideep Srivastava, and Osmar R. Zaiane, editors, Fourth WEBKDD Workshop: Web Mining for Usage Patterns & User Profiles, pages 100--114, Edmonton, Canada, July 2002. [ .pdf ]